Abstract: The interpretation of continuous glucose monitoring(CGM) signals for diabetic patients is crucial for glycemic management.However,large language model(LLM) for CGM is found the deficiency of high misinterpretation rates and the generation that conflicts with actual glucose due to the poor understanding of time series.To address these issues,the time series language model for CGM interpretation(CGM-TSLM) and a CGM interpretation dataset(GLiDCGM) of “glucose series-language description” pairs are proposed.A one-dimensional convolutional neural network time-series encoder is involved to capture the key quantitative features of the CGM series,a language model is employed to encode prompt instructions and generate matching language,and an attention mechanism is introduced to align and fuse features.Finally,the transport of the CGM series to text is completed by the multimodal supervised fine-tuning method.For the GLiDCGM dataset construction,the fuzzy logic text annotation method is adopted to generate initial descriptions of curve features,and optimized into the concise and accurate medical summaries by LLM.Experiments on the GLiDCGM dataset show that,the description generation of CGM-TSLM model is superior than baseline models such as LLaMA2-7B-Chat,Qwen,T5,and BART,with average improvements of 12.22 percentage points and 14.45 percentage points on word overlap and text similarity,respectively.Experimental results prove that CGM-TSLM is able to further enhance the generation ability of CGM series summaries and provide theoretical and data support for the analysis of wearable devices physiological data.

Key words: Continuous glucose monitoring, Time series language model, Multimodal information processing, Time series to text, Wearable devices